Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations232725
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory125.1 MiB
Average record size in memory563.8 B

Variable types

Categorical4
Text3
Numeric11

Alerts

acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
time_signature is highly imbalanced (69.0%) Imbalance
popularity has 6312 (2.7%) zeros Zeros
instrumentalness has 79236 (34.0%) zeros Zeros

Reproduction

Analysis started2025-04-21 01:29:11.350646
Analysis finished2025-04-21 01:29:46.762352
Duration35.41 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

genre
Categorical

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 MiB
Comedy
 
9681
Soundtrack
 
9646
Indie
 
9543
Jazz
 
9441
Pop
 
9386
Other values (22)
185028 

Length

Max length16
Median length10
Mean length6.4202084
Min length3

Characters and Unicode

Total characters1494143
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMovie
2nd rowMovie
3rd rowMovie
4th rowMovie
5th rowMovie

Common Values

ValueCountFrequency (%)
Comedy 9681
 
4.2%
Soundtrack 9646
 
4.1%
Indie 9543
 
4.1%
Jazz 9441
 
4.1%
Pop 9386
 
4.0%
Electronic 9377
 
4.0%
Children’s Music 9353
 
4.0%
Folk 9299
 
4.0%
Hip-Hop 9295
 
4.0%
Rock 9272
 
4.0%
Other values (17) 138432
59.5%

Length

2025-04-20T22:29:46.967333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
music 14756
 
6.0%
comedy 9681
 
3.9%
soundtrack 9646
 
3.9%
indie 9543
 
3.9%
jazz 9441
 
3.8%
pop 9386
 
3.8%
electronic 9377
 
3.8%
children’s 9353
 
3.8%
folk 9299
 
3.8%
hip-hop 9295
 
3.8%
Other values (19) 147823
59.7%

Most occurring characters

ValueCountFrequency (%)
e 140144
 
9.4%
o 109538
 
7.3%
a 99885
 
6.7%
i 92988
 
6.2%
l 88653
 
5.9%
n 87813
 
5.9%
c 70385
 
4.7%
r 69082
 
4.6%
s 57047
 
3.8%
t 55140
 
3.7%
Other values (30) 623468
41.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1494143
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 140144
 
9.4%
o 109538
 
7.3%
a 99885
 
6.7%
i 92988
 
6.2%
l 88653
 
5.9%
n 87813
 
5.9%
c 70385
 
4.7%
r 69082
 
4.6%
s 57047
 
3.8%
t 55140
 
3.7%
Other values (30) 623468
41.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1494143
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 140144
 
9.4%
o 109538
 
7.3%
a 99885
 
6.7%
i 92988
 
6.2%
l 88653
 
5.9%
n 87813
 
5.9%
c 70385
 
4.7%
r 69082
 
4.6%
s 57047
 
3.8%
t 55140
 
3.7%
Other values (30) 623468
41.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1494143
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 140144
 
9.4%
o 109538
 
7.3%
a 99885
 
6.7%
i 92988
 
6.2%
l 88653
 
5.9%
n 87813
 
5.9%
c 70385
 
4.7%
r 69082
 
4.6%
s 57047
 
3.8%
t 55140
 
3.7%
Other values (30) 623468
41.7%
Distinct14564
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size15.6 MiB
2025-04-20T22:29:47.328325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length84
Median length48
Mean length12.078436
Min length1

Characters and Unicode

Total characters2810954
Distinct characters267
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3703 ?
Unique (%)1.6%

Sample

1st rowHenri Salvador
2nd rowMartin & les fées
3rd rowJoseph Williams
4th rowHenri Salvador
5th rowFabien Nataf
ValueCountFrequency (%)
the 19067
 
4.1%
5474
 
1.2%
music 2902
 
0.6%
john 2341
 
0.5%
of 1928
 
0.4%
and 1563
 
0.3%
giuseppe 1449
 
0.3%
band 1435
 
0.3%
black 1397
 
0.3%
verdi 1394
 
0.3%
Other values (15108) 430482
91.7%
2025-04-20T22:29:47.757437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 245766
 
8.7%
236707
 
8.4%
a 216096
 
7.7%
i 174430
 
6.2%
n 166886
 
5.9%
o 165084
 
5.9%
r 149450
 
5.3%
l 116453
 
4.1%
s 115170
 
4.1%
t 99269
 
3.5%
Other values (257) 1125643
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2810954
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 245766
 
8.7%
236707
 
8.4%
a 216096
 
7.7%
i 174430
 
6.2%
n 166886
 
5.9%
o 165084
 
5.9%
r 149450
 
5.3%
l 116453
 
4.1%
s 115170
 
4.1%
t 99269
 
3.5%
Other values (257) 1125643
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2810954
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 245766
 
8.7%
236707
 
8.4%
a 216096
 
7.7%
i 174430
 
6.2%
n 166886
 
5.9%
o 165084
 
5.9%
r 149450
 
5.3%
l 116453
 
4.1%
s 115170
 
4.1%
t 99269
 
3.5%
Other values (257) 1125643
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2810954
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 245766
 
8.7%
236707
 
8.4%
a 216096
 
7.7%
i 174430
 
6.2%
n 166886
 
5.9%
o 165084
 
5.9%
r 149450
 
5.3%
l 116453
 
4.1%
s 115170
 
4.1%
t 99269
 
3.5%
Other values (257) 1125643
40.0%
Distinct148614
Distinct (%)63.9%
Missing1
Missing (%)< 0.1%
Memory size17.9 MiB
2025-04-20T22:29:48.049888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length292
Median length207
Mean length20.054322
Min length1

Characters and Unicode

Total characters4667122
Distinct characters1735
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112049 ?
Unique (%)48.1%

Sample

1st rowC'est beau de faire un Show
2nd rowPerdu d'avance (par Gad Elmaleh)
3rd rowDon't Let Me Be Lonely Tonight
4th rowDis-moi Monsieur Gordon Cooper
5th rowOuverture
ValueCountFrequency (%)
35552
 
4.1%
the 27070
 
3.1%
feat 11149
 
1.3%
in 11142
 
1.3%
you 10856
 
1.2%
i 10716
 
1.2%
a 9368
 
1.1%
of 8906
 
1.0%
me 8361
 
1.0%
to 6857
 
0.8%
Other values (62786) 731760
83.9%
2025-04-20T22:29:48.514836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
639013
 
13.7%
e 416386
 
8.9%
o 278913
 
6.0%
a 271552
 
5.8%
i 235471
 
5.0%
n 228461
 
4.9%
t 210209
 
4.5%
r 206331
 
4.4%
s 150657
 
3.2%
l 147669
 
3.2%
Other values (1725) 1882460
40.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4667122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
639013
 
13.7%
e 416386
 
8.9%
o 278913
 
6.0%
a 271552
 
5.8%
i 235471
 
5.0%
n 228461
 
4.9%
t 210209
 
4.5%
r 206331
 
4.4%
s 150657
 
3.2%
l 147669
 
3.2%
Other values (1725) 1882460
40.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4667122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
639013
 
13.7%
e 416386
 
8.9%
o 278913
 
6.0%
a 271552
 
5.8%
i 235471
 
5.0%
n 228461
 
4.9%
t 210209
 
4.5%
r 206331
 
4.4%
s 150657
 
3.2%
l 147669
 
3.2%
Other values (1725) 1882460
40.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4667122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
639013
 
13.7%
e 416386
 
8.9%
o 278913
 
6.0%
a 271552
 
5.8%
i 235471
 
5.0%
n 228461
 
4.9%
t 210209
 
4.5%
r 206331
 
4.4%
s 150657
 
3.2%
l 147669
 
3.2%
Other values (1725) 1882460
40.3%
Distinct176774
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size17.5 MiB
2025-04-20T22:29:48.773633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters5119950
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141650 ?
Unique (%)60.9%

Sample

1st row0BRjO6ga9RKCKjfDqeFgWV
2nd row0BjC1NfoEOOusryehmNudP
3rd row0CoSDzoNIKCRs124s9uTVy
4th row0Gc6TVm52BwZD07Ki6tIvf
5th row0IuslXpMROHdEPvSl1fTQK
ValueCountFrequency (%)
3r73y7x53miqzwnklowq5i 8
 
< 0.1%
6svqnuvcvftxvlk3ec0ngd 8
 
< 0.1%
0ue0rhnraeysiygxpylozc 8
 
< 0.1%
6aite2iej1qklaofpjczw1 8
 
< 0.1%
3ussjndmmoyeraak9kvpjr 8
 
< 0.1%
0wy9ra9fjkuesyym9uzvk5 8
 
< 0.1%
371h6hjs4sxgbq9ivffuil 7
 
< 0.1%
1z2mfax1nj09nzgjodnvrw 7
 
< 0.1%
7cev9vwa8xo9wwxtxgykvy 7
 
< 0.1%
03wkmrnyvvw6s9nm4i4jus 7
 
< 0.1%
Other values (176764) 232649
> 99.9%
2025-04-20T22:29:49.140348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 109277
 
2.1%
3 109068
 
2.1%
1 108973
 
2.1%
6 108844
 
2.1%
2 108817
 
2.1%
5 108714
 
2.1%
4 108501
 
2.1%
7 102837
 
2.0%
L 79496
 
1.6%
y 79463
 
1.6%
Other values (52) 4095960
80.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5119950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 109277
 
2.1%
3 109068
 
2.1%
1 108973
 
2.1%
6 108844
 
2.1%
2 108817
 
2.1%
5 108714
 
2.1%
4 108501
 
2.1%
7 102837
 
2.0%
L 79496
 
1.6%
y 79463
 
1.6%
Other values (52) 4095960
80.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5119950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 109277
 
2.1%
3 109068
 
2.1%
1 108973
 
2.1%
6 108844
 
2.1%
2 108817
 
2.1%
5 108714
 
2.1%
4 108501
 
2.1%
7 102837
 
2.0%
L 79496
 
1.6%
y 79463
 
1.6%
Other values (52) 4095960
80.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5119950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 109277
 
2.1%
3 109068
 
2.1%
1 108973
 
2.1%
6 108844
 
2.1%
2 108817
 
2.1%
5 108714
 
2.1%
4 108501
 
2.1%
7 102837
 
2.0%
L 79496
 
1.6%
y 79463
 
1.6%
Other values (52) 4095960
80.0%

popularity
Real number (ℝ)

Zeros 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.127502
Minimum0
Maximum100
Zeros6312
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-04-20T22:29:49.302815image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q129
median43
Q355
95-th percentile68
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.189948
Coefficient of variation (CV)0.44228184
Kurtosis-0.40151882
Mean41.127502
Median Absolute Deviation (MAD)13
Skewness-0.33638987
Sum9571398
Variance330.87419
MonotonicityNot monotonic
2025-04-20T22:29:49.452211image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6312
 
2.7%
50 5415
 
2.3%
53 5414
 
2.3%
51 5401
 
2.3%
52 5342
 
2.3%
49 5266
 
2.3%
48 5068
 
2.2%
54 5013
 
2.2%
47 4944
 
2.1%
55 4696
 
2.0%
Other values (91) 179854
77.3%
ValueCountFrequency (%)
0 6312
2.7%
1 1289
 
0.6%
2 903
 
0.4%
3 817
 
0.4%
4 811
 
0.3%
5 915
 
0.4%
6 789
 
0.3%
7 774
 
0.3%
8 921
 
0.4%
9 986
 
0.4%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 4
 
< 0.1%
98 3
 
< 0.1%
97 10
 
< 0.1%
96 8
 
< 0.1%
95 11
 
< 0.1%
94 7
 
< 0.1%
93 11
 
< 0.1%
92 21
< 0.1%
91 30
< 0.1%

acousticness
Real number (ℝ)

High correlation 

Distinct4734
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36856045
Minimum0
Maximum0.996
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-04-20T22:29:49.600739image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000786
Q10.0376
median0.232
Q30.722
95-th percentile0.974
Maximum0.996
Range0.996
Interquartile range (IQR)0.6844

Descriptive statistics

Standard deviation0.35476804
Coefficient of variation (CV)0.96257761
Kurtosis-1.2850897
Mean0.36856045
Median Absolute Deviation (MAD)0.22428
Skewness0.53424209
Sum85773.231
Variance0.12586036
MonotonicityNot monotonic
2025-04-20T22:29:49.745382image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 851
 
0.4%
0.994 701
 
0.3%
0.992 682
 
0.3%
0.993 646
 
0.3%
0.991 597
 
0.3%
0.99 571
 
0.2%
0.985 525
 
0.2%
0.987 518
 
0.2%
0.989 510
 
0.2%
0.983 506
 
0.2%
Other values (4724) 226618
97.4%
ValueCountFrequency (%)
0 1
< 0.1%
1 × 10-61
< 0.1%
1.02 × 10-61
< 0.1%
1.08 × 10-61
< 0.1%
1.12 × 10-61
< 0.1%
1.18 × 10-61
< 0.1%
1.21 × 10-61
< 0.1%
1.27 × 10-61
< 0.1%
1.28 × 10-62
< 0.1%
1.3 × 10-62
< 0.1%
ValueCountFrequency (%)
0.996 255
 
0.1%
0.995 851
0.4%
0.994 701
0.3%
0.993 646
0.3%
0.992 682
0.3%
0.991 597
0.3%
0.99 571
0.2%
0.989 510
0.2%
0.988 500
0.2%
0.987 518
0.2%

danceability
Real number (ℝ)

High correlation 

Distinct1295
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55436447
Minimum0.0569
Maximum0.989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-04-20T22:29:49.886520image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0569
5-th percentile0.206
Q10.435
median0.571
Q30.692
95-th percentile0.833
Maximum0.989
Range0.9321
Interquartile range (IQR)0.257

Descriptive statistics

Standard deviation0.18560823
Coefficient of variation (CV)0.33481263
Kurtosis-0.36613688
Mean0.55436447
Median Absolute Deviation (MAD)0.127
Skewness-0.37827814
Sum129014.47
Variance0.034450413
MonotonicityNot monotonic
2025-04-20T22:29:50.026971image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.597 558
 
0.2%
0.547 544
 
0.2%
0.589 542
 
0.2%
0.61 542
 
0.2%
0.622 540
 
0.2%
0.529 539
 
0.2%
0.626 536
 
0.2%
0.657 536
 
0.2%
0.628 535
 
0.2%
0.623 535
 
0.2%
Other values (1285) 227318
97.7%
ValueCountFrequency (%)
0.0569 1
 
< 0.1%
0.057 1
 
< 0.1%
0.0572 1
 
< 0.1%
0.0573 1
 
< 0.1%
0.0577 1
 
< 0.1%
0.0581 2
< 0.1%
0.0582 2
< 0.1%
0.0584 1
 
< 0.1%
0.059 1
 
< 0.1%
0.0592 3
< 0.1%
ValueCountFrequency (%)
0.989 1
 
< 0.1%
0.987 3
< 0.1%
0.986 1
 
< 0.1%
0.985 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 2
 
< 0.1%
0.98 6
< 0.1%
0.979 3
< 0.1%
0.978 5
< 0.1%
0.977 3
< 0.1%

duration_ms
Real number (ℝ)

Distinct70749
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean235122.34
Minimum15387
Maximum5552917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-04-20T22:29:50.158793image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum15387
5-th percentile108036
Q1182857
median220427
Q3265768
95-th percentile395597.4
Maximum5552917
Range5537530
Interquartile range (IQR)82911

Descriptive statistics

Standard deviation118935.91
Coefficient of variation (CV)0.50584691
Kurtosis250.74254
Mean235122.34
Median Absolute Deviation (MAD)40867
Skewness9.8933756
Sum5.4718846 × 1010
Variance1.4145751 × 1010
MonotonicityNot monotonic
2025-04-20T22:29:50.303258image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240000 138
 
0.1%
180000 120
 
0.1%
192000 115
 
< 0.1%
216000 99
 
< 0.1%
200000 85
 
< 0.1%
186000 81
 
< 0.1%
208000 68
 
< 0.1%
210000 66
 
< 0.1%
198000 64
 
< 0.1%
228000 61
 
< 0.1%
Other values (70739) 231828
99.6%
ValueCountFrequency (%)
15387 1
 
< 0.1%
15509 1
 
< 0.1%
16316 1
 
< 0.1%
16640 3
< 0.1%
16748 1
 
< 0.1%
16760 1
 
< 0.1%
17000 1
 
< 0.1%
17213 1
 
< 0.1%
17627 1
 
< 0.1%
17840 1
 
< 0.1%
ValueCountFrequency (%)
5552917 1
< 0.1%
5488000 1
< 0.1%
4830606 1
< 0.1%
4830584 1
< 0.1%
4804015 1
< 0.1%
4791725 1
< 0.1%
4661991 1
< 0.1%
4497994 2
< 0.1%
4337529 1
< 0.1%
4303366 1
< 0.1%

energy
Real number (ℝ)

High correlation 

Distinct2517
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57095767
Minimum2.03 × 10-5
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-04-20T22:29:50.666153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2.03 × 10-5
5-th percentile0.0802
Q10.385
median0.605
Q30.787
95-th percentile0.944
Maximum0.999
Range0.9989797
Interquartile range (IQR)0.402

Descriptive statistics

Standard deviation0.26345556
Coefficient of variation (CV)0.46142748
Kurtosis-0.81359495
Mean0.57095767
Median Absolute Deviation (MAD)0.197
Skewness-0.40022328
Sum132876.12
Variance0.069408833
MonotonicityNot monotonic
2025-04-20T22:29:50.819304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.721 417
 
0.2%
0.675 403
 
0.2%
0.72 392
 
0.2%
0.686 389
 
0.2%
0.738 389
 
0.2%
0.714 378
 
0.2%
0.843 376
 
0.2%
0.691 374
 
0.2%
0.73 367
 
0.2%
0.728 366
 
0.2%
Other values (2507) 228874
98.3%
ValueCountFrequency (%)
2.03 × 10-52
< 0.1%
9.8 × 10-51
< 0.1%
0.000216 1
< 0.1%
0.000234 1
< 0.1%
0.000243 2
< 0.1%
0.000259 1
< 0.1%
0.000263 2
< 0.1%
0.000267 1
< 0.1%
0.000273 1
< 0.1%
0.000412 1
< 0.1%
ValueCountFrequency (%)
0.999 17
 
< 0.1%
0.998 50
 
< 0.1%
0.997 58
 
< 0.1%
0.996 111
< 0.1%
0.995 163
0.1%
0.994 138
0.1%
0.993 143
0.1%
0.992 142
0.1%
0.991 159
0.1%
0.99 182
0.1%

instrumentalness
Real number (ℝ)

Zeros 

Distinct5400
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14830123
Minimum0
Maximum0.999
Zeros79236
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-04-20T22:29:50.967454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.43 × 10-5
Q30.0358
95-th percentile0.902
Maximum0.999
Range0.999
Interquartile range (IQR)0.0358

Descriptive statistics

Standard deviation0.30276836
Coefficient of variation (CV)2.0415768
Kurtosis1.5881785
Mean0.14830123
Median Absolute Deviation (MAD)4.43 × 10-5
Skewness1.8197664
Sum34513.405
Variance0.091668682
MonotonicityNot monotonic
2025-04-20T22:29:51.125144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79236
34.0%
0.912 235
 
0.1%
0.91 230
 
0.1%
0.923 222
 
0.1%
0.918 222
 
0.1%
0.914 217
 
0.1%
0.898 216
 
0.1%
0.911 216
 
0.1%
0.905 214
 
0.1%
0.899 211
 
0.1%
Other values (5390) 151506
65.1%
ValueCountFrequency (%)
0 79236
34.0%
1 × 10-633
 
< 0.1%
1.01 × 10-6104
 
< 0.1%
1.02 × 10-696
 
< 0.1%
1.03 × 10-681
 
< 0.1%
1.04 × 10-696
 
< 0.1%
1.05 × 10-6100
 
< 0.1%
1.06 × 10-684
 
< 0.1%
1.07 × 10-688
 
< 0.1%
1.08 × 10-679
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.998 1
 
< 0.1%
0.997 2
 
< 0.1%
0.996 3
 
< 0.1%
0.994 7
< 0.1%
0.993 8
< 0.1%
0.992 11
< 0.1%
0.991 9
< 0.1%
0.99 5
< 0.1%
0.989 11
< 0.1%

key
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
C
27583 
G
26390 
D
24077 
C#
23201 
A
22671 
Other values (7)
108803 

Length

Max length2
Median length1
Mean length1.3294618
Min length1

Characters and Unicode

Total characters309399
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC#
2nd rowF#
3rd rowC
4th rowC#
5th rowF

Common Values

ValueCountFrequency (%)
C 27583
11.9%
G 26390
11.3%
D 24077
10.3%
C# 23201
10.0%
A 22671
9.7%
F 20279
8.7%
B 17661
7.6%
E 17390
7.5%
A# 15526
6.7%
F# 15222
6.5%
Other values (2) 22725
9.8%

Length

2025-04-20T22:29:51.262688image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c 50784
21.8%
g 41549
17.9%
a 38197
16.4%
f 35501
15.3%
d 31643
13.6%
b 17661
 
7.6%
e 17390
 
7.5%

Most occurring characters

ValueCountFrequency (%)
# 76674
24.8%
C 50784
16.4%
G 41549
13.4%
A 38197
12.3%
F 35501
11.5%
D 31643
10.2%
B 17661
 
5.7%
E 17390
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 309399
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
# 76674
24.8%
C 50784
16.4%
G 41549
13.4%
A 38197
12.3%
F 35501
11.5%
D 31643
10.2%
B 17661
 
5.7%
E 17390
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 309399
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
# 76674
24.8%
C 50784
16.4%
G 41549
13.4%
A 38197
12.3%
F 35501
11.5%
D 31643
10.2%
B 17661
 
5.7%
E 17390
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 309399
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
# 76674
24.8%
C 50784
16.4%
G 41549
13.4%
A 38197
12.3%
F 35501
11.5%
D 31643
10.2%
B 17661
 
5.7%
E 17390
 
5.6%

liveness
Real number (ℝ)

Distinct1732
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21500928
Minimum0.00967
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-04-20T22:29:51.395718image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.00967
5-th percentile0.0628
Q10.0974
median0.128
Q30.264
95-th percentile0.7088
Maximum1
Range0.99033
Interquartile range (IQR)0.1666

Descriptive statistics

Standard deviation0.19827258
Coefficient of variation (CV)0.92215825
Kurtosis3.8879733
Mean0.21500928
Median Absolute Deviation (MAD)0.0466
Skewness2.074093
Sum50038.036
Variance0.039312018
MonotonicityNot monotonic
2025-04-20T22:29:51.542050image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 2860
 
1.2%
0.11 2702
 
1.2%
0.108 2608
 
1.1%
0.109 2537
 
1.1%
0.107 2451
 
1.1%
0.112 2437
 
1.0%
0.106 2332
 
1.0%
0.105 2323
 
1.0%
0.104 2259
 
1.0%
0.102 2164
 
0.9%
Other values (1722) 208052
89.4%
ValueCountFrequency (%)
0.00967 2
< 0.1%
0.0102 1
< 0.1%
0.0105 1
< 0.1%
0.0119 1
< 0.1%
0.012 1
< 0.1%
0.0121 1
< 0.1%
0.0123 1
< 0.1%
0.0124 2
< 0.1%
0.013 1
< 0.1%
0.0136 1
< 0.1%
ValueCountFrequency (%)
1 6
< 0.1%
0.999 1
 
< 0.1%
0.998 3
 
< 0.1%
0.997 1
 
< 0.1%
0.996 9
< 0.1%
0.995 4
 
< 0.1%
0.994 4
 
< 0.1%
0.993 8
< 0.1%
0.992 10
< 0.1%
0.991 7
< 0.1%

loudness
Real number (ℝ)

High correlation 

Distinct27923
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.5698854
Minimum-52.457
Maximum3.744
Zeros2
Zeros (%)< 0.1%
Negative232632
Negative (%)> 99.9%
Memory size1.8 MiB
2025-04-20T22:29:51.677757image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-52.457
5-th percentile-22.397
Q1-11.771
median-7.762
Q3-5.501
95-th percentile-3.392
Maximum3.744
Range56.201
Interquartile range (IQR)6.27

Descriptive statistics

Standard deviation5.9982036
Coefficient of variation (CV)-0.62677904
Kurtosis3.2074673
Mean-9.5698854
Median Absolute Deviation (MAD)2.744
Skewness-1.6622084
Sum-2227151.6
Variance35.978447
MonotonicityNot monotonic
2025-04-20T22:29:51.816795image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.318 57
 
< 0.1%
-5.46 52
 
< 0.1%
-5.131 51
 
< 0.1%
-5.428 51
 
< 0.1%
-5.48 50
 
< 0.1%
-6.611 50
 
< 0.1%
-5.564 49
 
< 0.1%
-5.597 49
 
< 0.1%
-6.078 49
 
< 0.1%
-5.133 48
 
< 0.1%
Other values (27913) 232219
99.8%
ValueCountFrequency (%)
-52.457 1
< 0.1%
-47.669 1
< 0.1%
-47.599 1
< 0.1%
-47.499 1
< 0.1%
-47.432 1
< 0.1%
-47.046 1
< 0.1%
-46.985 1
< 0.1%
-46.507 1
< 0.1%
-46.122 1
< 0.1%
-46.052 1
< 0.1%
ValueCountFrequency (%)
3.744 1
 
< 0.1%
1.949 1
 
< 0.1%
1.893 1
 
< 0.1%
1.61 1
 
< 0.1%
1.585 1
 
< 0.1%
1.342 4
< 0.1%
1.314 1
 
< 0.1%
1.275 1
 
< 0.1%
1.258 1
 
< 0.1%
1.1 1
 
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
Major
151744 
Minor
80981 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1163625
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMajor
2nd rowMinor
3rd rowMinor
4th rowMajor
5th rowMajor

Common Values

ValueCountFrequency (%)
Major 151744
65.2%
Minor 80981
34.8%

Length

2025-04-20T22:29:51.943381image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T22:29:52.053489image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
major 151744
65.2%
minor 80981
34.8%

Most occurring characters

ValueCountFrequency (%)
M 232725
20.0%
o 232725
20.0%
r 232725
20.0%
a 151744
13.0%
j 151744
13.0%
i 80981
 
7.0%
n 80981
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1163625
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 232725
20.0%
o 232725
20.0%
r 232725
20.0%
a 151744
13.0%
j 151744
13.0%
i 80981
 
7.0%
n 80981
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1163625
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 232725
20.0%
o 232725
20.0%
r 232725
20.0%
a 151744
13.0%
j 151744
13.0%
i 80981
 
7.0%
n 80981
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1163625
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 232725
20.0%
o 232725
20.0%
r 232725
20.0%
a 151744
13.0%
j 151744
13.0%
i 80981
 
7.0%
n 80981
 
7.0%

speechiness
Real number (ℝ)

Distinct1641
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12076501
Minimum0.0222
Maximum0.967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-04-20T22:29:52.185479image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.0222
5-th percentile0.0286
Q10.0367
median0.0501
Q30.105
95-th percentile0.448
Maximum0.967
Range0.9448
Interquartile range (IQR)0.0683

Descriptive statistics

Standard deviation0.18551831
Coefficient of variation (CV)1.5361925
Kurtosis10.98476
Mean0.12076501
Median Absolute Deviation (MAD)0.0178
Skewness3.3112688
Sum28105.038
Variance0.034417042
MonotonicityNot monotonic
2025-04-20T22:29:52.383356image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0374 663
 
0.3%
0.0332 654
 
0.3%
0.0337 652
 
0.3%
0.0363 650
 
0.3%
0.0343 642
 
0.3%
0.0323 642
 
0.3%
0.0328 635
 
0.3%
0.0362 633
 
0.3%
0.0329 626
 
0.3%
0.0349 625
 
0.3%
Other values (1631) 226303
97.2%
ValueCountFrequency (%)
0.0222 2
 
< 0.1%
0.0223 1
 
< 0.1%
0.0224 6
 
< 0.1%
0.0225 5
 
< 0.1%
0.0226 5
 
< 0.1%
0.0227 3
 
< 0.1%
0.0228 20
< 0.1%
0.0229 17
< 0.1%
0.023 6
 
< 0.1%
0.0231 23
< 0.1%
ValueCountFrequency (%)
0.967 1
 
< 0.1%
0.965 11
 
< 0.1%
0.964 13
 
< 0.1%
0.963 21
 
< 0.1%
0.962 42
< 0.1%
0.961 41
< 0.1%
0.96 71
< 0.1%
0.959 59
< 0.1%
0.958 73
< 0.1%
0.957 92
< 0.1%

tempo
Real number (ℝ)

Distinct78512
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.66658
Minimum30.379
Maximum242.903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-04-20T22:29:52.528009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum30.379
5-th percentile74.984
Q192.959
median115.778
Q3139.054
95-th percentile174.0058
Maximum242.903
Range212.524
Interquartile range (IQR)46.095

Descriptive statistics

Standard deviation30.898907
Coefficient of variation (CV)0.26259712
Kurtosis-0.46687809
Mean117.66658
Median Absolute Deviation (MAD)23.033
Skewness0.40334977
Sum27383956
Variance954.74243
MonotonicityNot monotonic
2025-04-20T22:29:52.673988image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.016 61
 
< 0.1%
100.003 60
 
< 0.1%
100.014 60
 
< 0.1%
120.008 59
 
< 0.1%
120.003 59
 
< 0.1%
120.012 58
 
< 0.1%
120.015 55
 
< 0.1%
119.994 54
 
< 0.1%
120 52
 
< 0.1%
130.018 52
 
< 0.1%
Other values (78502) 232155
99.8%
ValueCountFrequency (%)
30.379 1
< 0.1%
31.033 1
< 0.1%
31.689 1
< 0.1%
31.988 1
< 0.1%
32.08 1
< 0.1%
32.244 1
< 0.1%
32.451 1
< 0.1%
32.509 1
< 0.1%
33.593 1
< 0.1%
33.792 1
< 0.1%
ValueCountFrequency (%)
242.903 1
< 0.1%
239.848 1
< 0.1%
236.799 1
< 0.1%
236.735 1
< 0.1%
235.446 1
< 0.1%
234.923 1
< 0.1%
232.69 1
< 0.1%
232.602 1
< 0.1%
230.512 1
< 0.1%
229.886 1
< 0.1%

time_signature
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.3 MiB
4/4
200760 
3/4
24111 
5/4
 
5238
1/4
 
2608
0/4
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters698175
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4/4
2nd row4/4
3rd row5/4
4th row4/4
5th row4/4

Common Values

ValueCountFrequency (%)
4/4 200760
86.3%
3/4 24111
 
10.4%
5/4 5238
 
2.3%
1/4 2608
 
1.1%
0/4 8
 
< 0.1%

Length

2025-04-20T22:29:52.805498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T22:29:52.911929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
4/4 200760
86.3%
3/4 24111
 
10.4%
5/4 5238
 
2.3%
1/4 2608
 
1.1%
0/4 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
4 433485
62.1%
/ 232725
33.3%
3 24111
 
3.5%
5 5238
 
0.8%
1 2608
 
0.4%
0 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 698175
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 433485
62.1%
/ 232725
33.3%
3 24111
 
3.5%
5 5238
 
0.8%
1 2608
 
0.4%
0 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 698175
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 433485
62.1%
/ 232725
33.3%
3 24111
 
3.5%
5 5238
 
0.8%
1 2608
 
0.4%
0 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 698175
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 433485
62.1%
/ 232725
33.3%
3 24111
 
3.5%
5 5238
 
0.8%
1 2608
 
0.4%
0 8
 
< 0.1%

valence
Real number (ℝ)

High correlation 

Distinct1692
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45491686
Minimum0
Maximum1
Zeros32
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.8 MiB
2025-04-20T22:29:53.051272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0514
Q10.237
median0.444
Q30.66
95-th percentile0.895
Maximum1
Range1
Interquartile range (IQR)0.423

Descriptive statistics

Standard deviation0.26006548
Coefficient of variation (CV)0.57167696
Kurtosis-1.0135752
Mean0.45491686
Median Absolute Deviation (MAD)0.211
Skewness0.1441308
Sum105870.53
Variance0.067634056
MonotonicityNot monotonic
2025-04-20T22:29:53.199712image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 479
 
0.2%
0.962 403
 
0.2%
0.963 368
 
0.2%
0.37 363
 
0.2%
0.358 363
 
0.2%
0.377 359
 
0.2%
0.352 358
 
0.2%
0.398 358
 
0.2%
0.357 357
 
0.2%
0.338 341
 
0.1%
Other values (1682) 228976
98.4%
ValueCountFrequency (%)
0 32
< 0.1%
0.0124 1
 
< 0.1%
0.0141 1
 
< 0.1%
0.0176 1
 
< 0.1%
0.0178 1
 
< 0.1%
0.018 1
 
< 0.1%
0.0181 1
 
< 0.1%
0.0187 1
 
< 0.1%
0.0193 1
 
< 0.1%
0.0201 1
 
< 0.1%
ValueCountFrequency (%)
1 6
< 0.1%
0.999 1
 
< 0.1%
0.998 1
 
< 0.1%
0.996 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.992 2
 
< 0.1%
0.991 5
< 0.1%
0.99 5
< 0.1%
0.989 9
< 0.1%

Interactions

2025-04-20T22:29:43.375534image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:24.452967image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:26.252475image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:28.087139image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:29.909248image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:31.888885image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:33.786871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:35.655204image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:37.504471image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:39.492361image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:41.371505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:43.542861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:24.609289image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:26.413453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:28.248233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:30.069077image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:32.069184image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:33.953060image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:35.836978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:37.667714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:39.652445image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:41.534233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:43.708487image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:24.775352image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:26.574393image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:28.405919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:30.244115image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:32.233458image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:34.126405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:36.001086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:37.834739image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:39.832423image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:41.684768image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:43.869956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:24.937601image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:26.736447image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:28.566324image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:30.574464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:32.401907image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:34.300826image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:36.168242image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:37.989603image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:40.006832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:41.850279image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:44.045895image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:25.098637image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:26.921953image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:28.730596image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:30.732339image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:32.566245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:34.464049image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:36.332457image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:38.161979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:40.170590image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:42.232421image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:44.213867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:25.251358image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:27.097308image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:28.886691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:30.904828image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:32.732171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:34.655982image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:36.492414image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:38.373744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:40.345692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:42.401542image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:44.384074image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:25.445554image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:27.268027image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:29.058665image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:31.064467image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:32.894611image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:34.812654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:36.640540image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:38.574464image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:40.521459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:42.568764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:44.581761image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:25.612065image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:27.424141image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:29.211263image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:31.231627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:33.071025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:34.984430image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:36.810003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:38.782733image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:40.691939image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:42.731937image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:44.744919image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:25.786970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:27.585656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:29.384299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:31.398513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:33.289896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:35.153788image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:36.996281image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:38.968174image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:40.861959image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:42.887947image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:44.910680image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:25.948877image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:27.747646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:29.544408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:31.557762image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:33.455219image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:35.323289image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:37.157517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:39.143003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:41.018747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:43.050086image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:45.078451image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:26.098499image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:27.922870image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:29.702216image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:31.711728image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:33.615900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:35.494893image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:37.336414image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:39.311703image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:41.186550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-04-20T22:29:43.209492image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-04-20T22:29:53.306301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
acousticnessdanceabilityduration_msenergygenreinstrumentalnesskeylivenessloudnessmodepopularityspeechinesstempotime_signaturevalence
acousticness1.000-0.243-0.080-0.7030.2890.1760.044-0.022-0.6710.063-0.319-0.086-0.2510.176-0.283
danceability-0.2431.000-0.0930.2210.273-0.3090.040-0.0600.2850.0880.2570.2500.0250.1530.530
duration_ms-0.080-0.0931.0000.0350.0630.0900.006-0.0340.0210.0070.074-0.1220.0090.021-0.147
energy-0.7030.2210.0351.0000.280-0.2860.0430.1860.8150.0620.1970.2670.2240.1530.417
genre0.2890.2730.0630.2801.0000.2240.0710.2110.2730.2120.4250.3410.1230.1910.224
instrumentalness0.176-0.3090.090-0.2860.2241.0000.025-0.155-0.3950.049-0.195-0.293-0.0620.074-0.282
key0.0440.0400.0060.0430.0710.0251.0000.0210.0370.2410.0340.0490.0170.0260.025
liveness-0.022-0.060-0.0340.1860.211-0.1550.0211.0000.0800.020-0.0960.189-0.0110.0760.025
loudness-0.6710.2850.0210.8150.273-0.3950.0370.0801.0000.0400.3360.1490.2240.1540.360
mode0.0630.0880.0070.0620.2120.0490.2410.0200.0401.0000.0830.0770.0140.0250.032
popularity-0.3190.2570.0740.1970.425-0.1950.034-0.0960.3360.0831.000-0.0120.0910.1090.075
speechiness-0.0860.250-0.1220.2670.341-0.2930.0490.1890.1490.077-0.0121.0000.0410.1300.151
tempo-0.2510.0250.0090.2240.123-0.0620.017-0.0110.2240.0140.0910.0411.0000.0720.133
time_signature0.1760.1530.0210.1530.1910.0740.0260.0760.1540.0250.1090.1300.0721.0000.125
valence-0.2830.530-0.1470.4170.224-0.2820.0250.0250.3600.0320.0750.1510.1330.1251.000

Missing values

2025-04-20T22:29:45.369002image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-20T22:29:45.860941image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

genreartist_nametrack_nametrack_idpopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalence
0MovieHenri SalvadorC'est beau de faire un Show0BRjO6ga9RKCKjfDqeFgWV00.611000.389993730.91000.00000C#0.3460-1.828Major0.0525166.9694/40.814
1MovieMartin & les féesPerdu d'avance (par Gad Elmaleh)0BjC1NfoEOOusryehmNudP10.246000.5901373730.73700.00000F#0.1510-5.559Minor0.0868174.0034/40.816
2MovieJoseph WilliamsDon't Let Me Be Lonely Tonight0CoSDzoNIKCRs124s9uTVy30.952000.6631702670.13100.00000C0.1030-13.879Minor0.036299.4885/40.368
3MovieHenri SalvadorDis-moi Monsieur Gordon Cooper0Gc6TVm52BwZD07Ki6tIvf00.703000.2401524270.32600.00000C#0.0985-12.178Major0.0395171.7584/40.227
4MovieFabien NatafOuverture0IuslXpMROHdEPvSl1fTQK40.950000.331826250.22500.12300F0.2020-21.150Major0.0456140.5764/40.390
5MovieHenri SalvadorLe petit souper aux chandelles0Mf1jKa8eNAf1a4PwTbizj00.749000.5781606270.09480.00000C#0.1070-14.970Major0.143087.4794/40.358
6MovieMartin & les féesPremières recherches (par Paul Ventimila, Lorie Pester, Véronique Jannot, Michèle Laroque & Gérard Lenorman)0NUiKYRd6jt1LKMYGkUdnZ20.344000.7032122930.27000.00000C#0.1050-12.675Major0.953082.8734/40.533
7MovieLaura MayneLet Me Let Go0PbIF9YVD505GutwotpB5C150.939000.4162400670.26900.00000F#0.1130-8.949Major0.028696.8274/40.274
8MovieChorusHelka0ST6uPfvaPpJLtQwhE6KfC00.001040.7342262000.48100.00086C0.0765-7.725Major0.0460125.0804/40.765
9MovieLe Club des JuniorsLes bisous des bisounours0VSqZ3KStsjcfERGdcWpFO100.319000.5981526940.70500.00125G0.3490-7.790Major0.0281137.4964/40.718
genreartist_nametrack_nametrack_idpopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalence
232715SoulEmily KingDown5cA0vB8c9FMOVDWyJHgf26420.550000.3942818530.3460.000002E0.1290-13.617Major0.063590.8314/40.436
232716SoulJohn LegendQuickly (feat. Brandy)1U0OMWvR89Cm20vCNar50f390.231000.7362226670.7010.000000A#0.2030-4.345Minor0.100099.9914/40.770
232717SoulBellyP.O.P.2gGqKJWfWbToha2YmDxnnj430.104000.8022011730.5160.000485D0.1050-9.014Major0.2130175.6664/40.482
232718SoulMuddy WatersI Just Want To Make Love To You - Electric Mud Album Version2HFczeynfKGiM9KF2z2K7K430.013600.2942582670.7390.004820C0.1380-7.167Major0.0434176.4024/40.945
232719SoulBobby "Blue" BlandI'll Take Care Of You - Single Version2iZf3EUedz9MPqbAvXdpdA320.566000.4231446670.3370.000000A#0.2760-13.092Minor0.043680.0234/40.497
232720SoulSlaveSon Of Slide2XGLdVl7lGeq8ksM6Al7jT390.003840.6873262400.7140.544000D0.0845-10.626Major0.0316115.5424/40.962
232721SoulJr Thomas & The VolcanosBurning Fire1qWZdkBl4UVPj9lK6HuuFM380.032900.7852824470.6830.000880E0.2370-6.944Minor0.0337113.8304/40.969
232722SoulMuddy Waters(I'm Your) Hoochie Coochie Man2ziWXUmQLrXTiYjCg2fZ2t470.901000.5171669600.4190.000000D0.0945-8.282Major0.148084.1354/40.813
232723SoulR.LUM.RWith My Words6EFsue2YbIG4Qkq8Zr9Rir440.262000.7452224420.7040.000000A0.3330-7.137Major0.1460100.0314/40.489
232724SoulMint ConditionYou Don't Have To Hurt No More34XO9RwPMKjbvRry54QzWn350.097300.7583230270.4700.000049G#0.0836-6.708Minor0.0287113.8974/40.479